Datasets:

dair-ai
/

emotion

	---
	annotations_creators:
	- machine-generated
	language_creators:
	- machine-generated
	language:
	- en
	license:
	- other
	multilinguality:
	- monolingual
	size_categories:
	- 10K<n<100K
	source_datasets:
	- original
	task_categories:
	- text-classification
	task_ids:
	- multi-class-classification
	paperswithcode_id: emotion
	pretty_name: Emotion
	tags:
	- emotion-classification
	dataset_info:
	- config_name: split
	features:
	- name: text
	dtype: string
	- name: label
	dtype:
	class_label:
	names:
	'0': sadness
	'1': joy
	'2': love
	'3': anger
	'4': fear
	'5': surprise
	splits:
	- name: train
	num_bytes: 1741597
	num_examples: 16000
	- name: validation
	num_bytes: 214703
	num_examples: 2000
	- name: test
	num_bytes: 217181
	num_examples: 2000
	download_size: 740883
	dataset_size: 2173481
	- config_name: unsplit
	features:
	- name: text
	dtype: string
	- name: label
	dtype:
	class_label:
	names:
	'0': sadness
	'1': joy
	'2': love
	'3': anger
	'4': fear
	'5': surprise
	splits:
	- name: train
	num_bytes: 45445685
	num_examples: 416809
	download_size: 15388281
	dataset_size: 45445685
	train-eval-index:
	- config: default
	task: text-classification
	task_id: multi_class_classification
	splits:
	train_split: train
	eval_split: test
	col_mapping:
	text: text
	label: target
	metrics:
	- type: accuracy
	name: Accuracy
	- type: f1
	name: F1 macro
	args:
	average: macro
	- type: f1
	name: F1 micro
	args:
	average: micro
	- type: f1
	name: F1 weighted
	args:
	average: weighted
	- type: precision
	name: Precision macro
	args:
	average: macro
	- type: precision
	name: Precision micro
	args:
	average: micro
	- type: precision
	name: Precision weighted
	args:
	average: weighted
	- type: recall
	name: Recall macro
	args:
	average: macro
	- type: recall
	name: Recall micro
	args:
	average: micro
	- type: recall
	name: Recall weighted
	args:
	average: weighted
	---

	# Dataset Card for "emotion"

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset)
	- Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
	- Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
	- Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
	- Size of downloaded dataset files: 16.13 MB
	- Size of the generated dataset: 47.62 MB
	- Total amount of disk used: 63.75 MB

	### Dataset Summary

	Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.

	### Supported Tasks and Leaderboards

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Languages

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Dataset Structure

	### Data Instances

	An example looks as follows.
	```
	{
	"text": "im feeling quite sad and sorry for myself but ill snap out of it soon",
	"label": 0
	}
	```

	### Data Fields

	The data fields are:
	- `text`: a `string` feature.
	- `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5).
	- ```
	{
	0: 'sadness'
	1: 'joy'
	2: 'love'
	3: 'anger'
	4: 'fear'
	5: 'surprise'
	}
	```

	### Data Splits

	The dataset has 2 configurations:
	- split: with a total of 20_000 examples split into train, validation and split
	- unsplit: with a total of 416_809 examples in a single train split

	\| name \| train \| validation \| test \|
	\|---------\|-------:\|-----------:\|-----:\|
	\| split \| 16000 \| 2000 \| 2000 \|
	\| unsplit \| 416809 \| n/a \| n/a \|

	## Dataset Creation

	### Curation Rationale

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Source Data

	#### Initial Data Collection and Normalization

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	#### Who are the source language producers?

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Annotations

	#### Annotation process

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	#### Who are the annotators?

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Personal and Sensitive Information

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Discussion of Biases

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Other Known Limitations

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Additional Information

	### Dataset Curators

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Licensing Information

	The dataset should be used for educational and research purposes only.

	### Citation Information

	If you use this dataset, please cite:
	```
	@inproceedings{saravia-etal-2018-carer,
	title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
	author = "Saravia, Elvis and
	Liu, Hsien-Chi Toby and
	Huang, Yen-Hao and
	Wu, Junlin and
	Chen, Yi-Shin",
	booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
	month = oct # "-" # nov,
	year = "2018",
	address = "Brussels, Belgium",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/D18-1404",
	doi = "10.18653/v1/D18-1404",
	pages = "3687--3697",
	abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
	}
	```

	### Contributions

	Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.